The present application relates generally to medical imaging. It finds particular application in conjunction with magnetic resonance (MR) systems, and will be described with particular reference thereto. However, it is to be understood that it also finds application in other usage scenarios and is not necessarily limited to the aforementioned application.
Imaging by emission tomography, such as positron emission tomography (PET) or single photon emission computed tomography (SPECT), is enhanced by accounting for absorption in the imaged subject using a suitable attenuation map. Emission tomography performed in combination with transmissive computed tomography (CT) advantageously benefits from the availability of radiation attenuation data provided by the CT modality. A reconstructed CT image is essentially an attenuation map of the imaged subject for the x ray radiation used in generating the CT image data. Although the x ray radiation used in CT is generally not identical with the 511 keV radiation measured in PET or the emissions measured in SPECT or other emission tomography techniques, it is known that an attenuation map for the emission tomography can be generated from the reconstructed transmission CT image by suitably scaling the CT grayscale levels to account for the differences in radiation type.
In PET, annihilation photons are attenuated and scattered by the patient's body. For attenuation and scatter correction, an attenuation map is usually required in PET. In PET/CT imaging, this can be obtained from the CT data, however in PET/MR imaging, specifically simultaneous PET/MRI, CT images are not available. The attenuation map may instead be obtained from segmented MR images where the MR images are divided into segments, and each segment is assigned a tissue class and a pre-defined attenuation coefficient. The contrast mechanism in MR is fundamentally different from that of PET or SPECT (or CT, for that matter). As a consequence, it cannot be said that a “dark” MR pixel necessarily corresponds to either high or low attenuation. For example, bone tissue and air have similar grayscale intensities for some MR imaging modes, but the attenuation of emission radiation by bone is much higher than the attenuation by air. Correct attenuation coefficients of each tissue classes are needed for accurate attenuation correction.
However, lung tissue has varying attenuation properties that vary from patient to patient. Thus, for optimal attenuation correction, the lung tissue attenuation coefficient should be estimated for each patient. For example, this can be achieved using a combination of MR image segmentation and maximum likelihood reconstruction of activity and attenuation, but this is still challenging. It has been demonstrated that there is a significant correlation between attenuation coefficients (as determined from CT data) and MRI signal intensities in a Turbo-FLASH sequence with a short echo time (TE) of 0.75 ms. It has also been hypothesized that Ultrashort-Echo-Time (UTE) imaging may reveal additional contrast or improved quantification in lung tissue: in UTE imaging, the MR signal is acquired as soon as possible (in the sub-100 μs range) after excitation to minimize the effect of extremely short signal decay times (in the order of milliseconds or sub-milliseconds in bones and lungs). For qualitative imaging of lung tissue, zero-echo time (ZTE) MR imaging has been used, both with hard pulse excitation and frequency sweep pulses.
It has been contemplated to generate an attenuation map using various techniques. In one contemplated approach, an attenuation “atlas” of a typical subject, for example of a typical human subject, is employed. The attenuation atlas identifies attenuation of various components or regions of the typical subject. However, actual subjects, such as actual human subjects, vary substantially, and it is not straightforward to adapt the attenuation atlas to a particular subject.
Further, computing accurate lung densities from MR scans is challenging. MR scans show the same signal intensity for air and bone and sometimes do not include enough signal from the lungs to generate accurate attenuation maps for the lungs. One technique requires the use of specific PET and MRI data (field of view covering and centered on the lungs; no movement of the patient to prevent motion artifacts; co-registered MRI data), which is not always available. Another approach requires a small field of view to allow for breath-hold imaging to prevent breathing motion artifacts, which impedes application of this technique in humans due to longer acquisitions times. Another approach is to use whole-body Ultrashort Echo Time imaging (UTE) to minimize echo times in an application, and use non-selective excitation pulses that can excite large regions of the patient's body combined with 3D spatial encoding and radial readout. However, using whole-body imaging can result in backfolding (i.e. wrap-around) artifacts. Alternatively, 2D UTE imaging, the selectivity of the slice-selective excitation pulse is not optimal due to constraints on pulse duration and gradient strength, impairing 2D UTE imaging performance.
Lung segmentation in MR images is important for accurate attenuation correction map generation. Detailed lung segmentation in whole body MR images for attenuation correction map generation is challenging. The presence of lesions in the lung must be considered as non-air organ, and is challenging to segment. Lung segmentation from MR images has been explored, but segmenting the lung with clear distinction of internal soft tissues and lesion poses many challenges. These challenges arise due to artefacts introduced inherently from MR scans. One potential application is to segment the soft tissues within the lungs accurately and then assign the appropriate attenuation coefficients to the segmented internal lung tissues classifying them as healthy or unhealthy. Challenges arise from inherent defects from MR scans and low resolution. Segmenting a lung as a whole for attenuation correction map generation may be incorrect if there is a lesion inside the lung.
The present application provides an innovative and adaptive approach to tune the algorithm accordingly to the MR scan and adapt a detailed segmentation based on certain parameters.